The present invention relates to x-ray computed tomography (“CT”) imaging systems and, more particularly, to a method for reducing artifacts resulting from highly attenuating materials in multi-slice helical CT.
In a computed tomography system, an x-ray source projects a fan-shaped beam which is collimated to lie within an X-Y plane of a Cartesian coordinate system, termed the “imaging plane.” The x-ray beam passes through the object being imaged, such as a medical patient, and impinges upon an array of radiation detectors. The intensity of the transmitted radiation is dependent upon the attenuation of the x-ray beam by the object and each detector produces a separate electrical signal that is a measurement of the beam attenuation. The attenuation measurements from all the detectors are acquired separately to produce the transmission profile at a particular view angle.
The source and detector array in a conventional CT system are rotated on a gantry within the imaging plane and around the object so that the angle at which the x-ray beam intersects the object constantly changes. A group of x-ray attenuation measurements from the detector array at a given angle is referred to as a “view,” and a “scan” of the object comprises a set of views acquired at different angular orientations during one revolution of the x-ray source and detector. In a 2D scan, data is processed to construct an image that corresponds to a two dimensional slice taken through the object. The prevailing method for reconstructing an image from 2D data is referred to in the art as the filtered backprojection technique. This process converts the attenuation measurements from a scan into integers called “CT numbers,” or “Hounsfield units”, which are used to control the brightness of a corresponding pixel on a display.
The term “generation” is used in CT to describe successively commercially available types of CT systems utilizing different modes of scanning motion and x-ray detection. More specifically, each generation is characterized by a particular geometry of scanning motion, scanning time, shape of the x-ray beam, and detector system.
The so-called third generation scanners use a wide, “divergent” fan beam. In fact, the angle of the beam may be wide enough to encompass most or all of an entire patient section without the need for a linear translation of the x-ray tube and detectors. The x-ray detectors, which form a large array of detectors, are rigidly aligned relative to the x-ray beam, and there are no translational motions at all. The tube and detector array are synchronously rotated about the patient through an angle of around 180-360 degrees. Thus, there is only one type of motion, allowing a much faster scanning time to be achieved. After one rotation, a single tomographic section is obtained.
Cone beam CT systems are similar to so-called third generation 2D CT systems in that the x-ray beam fans out, or diverges, in the plane of the imaging slices. In addition, however, the x-ray beam fans out in the perpendicular direction to acquire attenuation data for a plurality of image slices.
Metal implants are not uncommon in subjects receiving CT examinations and may produce severe image artifacts in the form of bright and dark streaks and dark shadows. Small metal objects that occupy only a small image region can produce artifacts that affect entire images, obscuring anatomical structures and significantly degrading diagnostic value. Metal artifacts arise from the data inconsistency between ideal models assumed by reconstruction algorithms and the actual CT signal, which has been contaminated by the metal, or other highly attenuating material. X-rays are highly attenuated by metals and other materials, which in turn amplifies factors that lead to data inconsistencies and, eventually, artifacts such as noise, beam hardening, scattering, and non-linear partial volume effects.
Noise in projection data may be reduced by increasing the tube output (“mAs”) of the x-ray source; however, such an approach delivers an increased radiation dose to a subject and does not correct for other data inconsistencies, including those that lead to streaking and shading artifacts. Image quality may also be improved by increasing the tube potential (“kVp”), which provides higher-energy photons that have improved penetrating capability and reduces beam hardening. While this method is common in scan protocols for subjects having metal implants, it offers minimal improvement in scan quality over scans performed using standard tube potentials.
Adaptive filtering may be used to reduce streaking artifacts caused by photon starvation by adaptively smoothing projection data based on noise level; however, this method cannot correct the severe data inconsistencies caused by highly attenuating materials, such as metals, for example, titanium, cobalt, and stainless steel. Iterative and wavelet methods, which utilize practical data models that characterize noise, beam-hardening, and the scattering processes of protons, have been employed to reduce metal artifacts. While promising, these methods are difficult to implement with the standard reconstruction algorithms in modern CT scanners and can be too computationally expensive for the large data sets generated by multi-slice CT scanners.
Metal artifacts may also be reduced by identifying data contaminated by metal artifacts and replacing it with estimated data. One such method involves segmenting regions containing metal signals in reconstructed images, reprojecting this region to localize metal-contaminated projection data, replacement the affected projection data, and reconstructing the resulting corrected data. This method allows the consistent determination of metal signals among all projection views when the metal region is accurately segmented in the initial reconstructed images. However, severe artifacts in the initial reconstructed images often preclude accurate metal segmentation and prevent effective artifact reduction. Furthermore, reprojection of the metal image to projection space is computationally expensive, especially for the large clinical datasets acquired with multi-slice helical CT, and is sometimes prevented by the unavailability of sufficient information regarding the data acquisition geometry.
Other methods that do not require the reprojection of image data to projection space typically involve the direct segmentation of metal in the projection data and the replacement of metal-contaminated data with values interpolated from neighboring data. Metal segmentation in projection data can be based on a sinusoidal model in the sinogram for small circularly-shaped metal and fan-beam configurations. For example, in circular cone-beam CT geometry, known data acquisition geometry can be used to calculate the track of the metal projections. Such approaches work well for simple data acquisition geometries and simple metal shapes, but these approaches cannot provide effective metal artifact reduction when using complicated data acquisition geometries, for example, multi-slice helical CT, or when imaging complicated metal shapes or anatomical features.
The direct segmentation of metal in projection views acquired using multi-slice helical CT, which are typically composed of matrices having a small longitudinal dimension (for example, 16-64 pixels for typical 16 or 64 slice scanners), is difficult for two primary reasons. First, large quantities of projection data are often acquired to cover a long helical scanning range. Therefore, most projections contain little or no metal content, decreasing a segmentation algorithm's ability to assess the metal content of a projection and segment metal consistently among all projections. Second, the large amount of data acquired during multi-slice helical CT scans, for example, 40,000-60,000 projection views for a typical chest-abdomen-pelvis scan, makes it extremely difficult for a human to review and control the segmentation process.
It would therefore be desirable to develop a method for performing multi-slice helical CT that provides improved reduction of artifacts associated with metals and other highly attenuating materials, increased computational efficiency, and improved control of the artifact reduction process.